OpenRaiser/Intern-Atlas huggingface dataset:
VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

Implicit surfaces via neural radiance fields (NeRF) have shown surprising accuracy in surface reconstruction. Despite their success in reconstructing richly textured surfaces, existing methods struggle with planar regions with weak textures, which account for the majority of indoor scenes. In this paper, we address indoor dense surface reconstruction by revisiting key aspects of NeRF in order to use the recently proposed Vector Field (VF) as the implicit representation. VF is defined by the unit vector directed to the nearest surface point. It therefore flips direction at the surface and equals to the explicit surface normals. Except for this flip, VF remains constant along planar surfaces and provides a strong inductive bias in representing planar surfaces. Concretely, we develop a novel density-VF relationship and a training scheme that allows us to learn VF via volume rendering By doing this, VF-NeRF can model large planar surfaces and sharp corners accurately. We show that, when depth cues are available, our method further improves and achieves state-of-the-art results in reconstructing indoor scenes and rendering novel views. We extensively evaluate VF-NeRF on indoor datasets and run ablations of its components.
Neighbor Overlay-Induced Graph Attention Network
Venue: arXiv cs.LG 2024
Year: 2024

Graph neural networks (GNNs) have garnered significant attention due to their ability to represent graph data. Among various GNN variants, graph attention network (GAT) stands out since it is able to dynamically learn the importance of different nodes. However, present GATs heavily rely on the smoothed node features to obtain the attention coefficients rather than graph structural information, which fails to provide crucial contextual cues for node representations. To address this issue, this study proposes a neighbor overlay-induced graph attention network (NO-GAT) with the following two-fold ideas: a) learning favorable structural information, i.e., overlaid neighbors, outside the node feature propagation process from an adjacency matrix; b) injecting the information of overlaid neighbors into the node feature propagation process to compute the attention coefficient jointly. Empirical studies on graph benchmark datasets indicate that the proposed NO-GAT consistently outperforms state-of-the-art models.
Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer
Venue: arXiv cs.CV 2024
Year: 2024

Visual retrieval systems face significant challenges when updating models with improved representations due to misalignment between the old and new representations. The costly and resource-intensive backfilling process involves recalculating feature vectors for images in the gallery set whenever a new model is introduced. To address this, prior research has explored backward-compatible training methods that enable direct comparisons between new and old representations without backfilling. Despite these advancements, achieving a balance between backward compatibility and the performance of independently trained models remains an open problem. In this paper, we address it by expanding the representation space with additional dimensions and learning an orthogonal transformation to achieve compatibility with old models and, at the same time, integrate new information. This transformation preserves the original feature space's geometry, ensuring that our model aligns with previous versions while also learning new data. Our Orthogonal Compatible Aligned (OCA) approach eliminates the need for re-indexing during model updates and ensures that features can be compared directly across different model updates without additional mapping functions. Experimental results on CIFAR-100 and ImageNet-1k demonstrate that our method not only maintains compatibility with previous models but also achieves state-of-the-art accuracy, outperforming several existing methods.
RollingCache: Using Runtime Behavior to Defend Against Cache Side Channel Attacks
Venue: arXiv cs.CR 2024
Year: 2024

Shared caches are vulnerable to side channel attacks through contention in cache sets. Besides being a simple source of information leak, these side channels form useful gadgets for more sophisticated attacks that compromise the security of shared systems. The fundamental design aspect that contention attacks exploit is the deterministic nature of the set of addresses contending for a cache set. In this paper, we present RollingCache, a cache design that defends against contention attacks by dynamically changing the set of addresses contending for cache sets. Unlike prior defenses, RollingCache does not rely on address encryption/decryption, data relocation, or cache partitioning. We use one level of indirection to implement dynamic mapping controlled by the whole-cache runtime behavior. Our solution does not depend on having defined security domains, and can defend against an attacker running on the same or another core. We evaluate RollingCache on ChampSim using the SPEC-2017 benchmark suite. Our security evaluation shows that our dynamic mapping removes the deterministic ability to identify the source of contention. The performance evaluation shows an impact of 1.67\% over a mix of workloads, with a corresponding
PsychoLex: Unveiling the Psychological Mind of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex, a suite of resources designed to enhance LLMs' proficiency in psychological tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice.
Towards Effective Authorship Attribution: Integrating Class-Incremental Learning
Venue: arXiv cs.IR 2024
Year: 2024

AA is the process of attributing an unidentified document to its true author from a predefined group of known candidates, each possessing multiple samples. The nature of AA necessitates accommodating emerging new authors, as each individual must be considered unique. This uniqueness can be attributed to various factors, including their stylistic preferences, areas of expertise, gender, cultural background, and other personal characteristics that influence their writing. These diverse attributes contribute to the distinctiveness of each author, making it essential for AA systems to recognize and account for these variations. However, current AA benchmarks commonly overlook this uniqueness and frame the problem as a closed-world classification, assuming a fixed number of authors throughout the system's lifespan and neglecting the inclusion of emerging new authors. This oversight renders the majority of existing approaches ineffective for real-world applications of AA, where continuous learning is essential. These inefficiencies manifest as current models either resist learning new authors or experience catastrophic forgetting, where the introduction of new data causes the models to lose previously acquired knowledge. To address these inefficiencies, we propose redefining AA as CIL, where new authors are introduced incrementally after the initial training phase, allowing the system to adapt and learn continuously. To achieve this, we briefly examine subsequent CIL approaches introduced in other domains. Moreover, we have adopted several well-known CIL methods, along with an examination of their strengths and weaknesses in the context of AA. Additionally, we outline potential future directions for advancing CIL AA systems. As a result, our paper can serve as a starting point for evolving AA systems from closed-world models to continual learning through CIL paradigms.
Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning
Venue: arXiv cs.IR 2024
Year: 2024

Bundle recommendation aims to enhance business profitability and user convenience by suggesting a set of interconnected items. In real-world scenarios, leveraging the impact of asymmetric item affiliations is crucial for effective bundle modeling and understanding user preferences. To address this, we present BunCa, a novel bundle recommendation approach employing item-level causation-enhanced multi-view learning. BunCa provides comprehensive representations of users and bundles through two views: the Coherent View, leveraging the Multi-Prospect Causation Network for causation-sensitive relations among items, and the Cohesive View, employing LightGCN for information propagation among users and bundles. Modeling user preferences and bundle construction combined from both views ensures rigorous cohesion in direct user-bundle interactions through the Cohesive View and captures explicit intents through the Coherent View. Simultaneously, the integration of concrete and discrete contrastive learning optimizes the consistency and self-discrimination of multi-view representations. Extensive experiments with BunCa on three benchmark datasets demonstrate the effectiveness of this novel research and validate our hypothesis.
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
Venue: arXiv cs.IR 2024
Year: 2024

Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenues, user satisfaction, and user retention. However, real-world production platforms often encompass various domains to cater for diverse customer needs. Traditional CTR prediction models struggle in multi-domain recommendation scenarios, facing challenges of data sparsity and disparate data distributions across domains. Existing multi-domain recommendation approaches introduce specific-domain modules for each domain, which partially address these issues but often significantly increase model parameters and lead to insufficient training. In this paper, we propose a Multi-domain Low-Rank Adaptive network (MLoRA) for CTR prediction, where we introduce a specialized LoRA module for each domain. This approach enhances the model's performance in multi-domain CTR prediction tasks and is able to be applied to various deep-learning models. We evaluate the proposed method on several multi-domain datasets. Experimental results demonstrate our MLoRA approach achieves a significant improvement compared with state-of-the-art baselines. Furthermore, we deploy it in the production environment of the Alibaba.COM. The online A/B testing results indicate the superiority and flexibility in real-world production environments. The code of our MLoRA is publicly available.
Graph Retrieval-Augmented Generation: A Survey
Venue: arXiv cs.AI 2024
Year: 2024

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as ``hallucination'', lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field. In order to track recent progress in this field, we set up a repository at \url{https://github.com/pengboci/GraphRAG-Survey}.
Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat Assistants
Venue: arXiv cs.IR 2024
Year: 2024

This work presents Retail-GPT, an open-source RAG-based chatbot designed to enhance user engagement in retail e-commerce by guiding users through product recommendations and assisting with cart operations. The system is cross-platform and adaptable to various e-commerce domains, avoiding reliance on specific chat applications or commercial activities. Retail-GPT engages in human-like conversations, interprets user demands, checks product availability, and manages cart operations, aiming to serve as a virtual sales agent and test the viability of such assistants across different retail businesses.
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Venue: arXiv cs.IR 2024
Year: 2024

Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting. Existing FedCF methods typically combine distributed Collaborative Filtering (CF) algorithms with privacy-preserving mechanisms, and then preserve personalized information into a user embedding vector. However, the user embedding is usually insufficient to preserve the rich information of the fine-grained personalization across heterogeneous clients. This paper proposes a novel personalized FedCF method by preserving users' personalized information into a latent variable and a neural model simultaneously. Specifically, we decompose the modeling of user knowledge into two encoders, each designed to capture shared knowledge and personalized knowledge separately. A personalized gating network is then applied to balance personalization and generalization between the global and local encoders. Moreover, to effectively train the proposed framework, we model the CF problem as a specialized Variational AutoEncoder (VAE) task by integrating user interaction vector reconstruction with missing value prediction. The decoder is trained to reconstruct the implicit feedback from items the user has interacted with, while also predicting items the user might be interested in but has not yet interacted with. Experimental results on benchmark datasets demonstrate that the proposed method outperforms other baseline methods, showcasing superior performance. Our code is available at https://github.com/mtics/FedDAE.
Creating Publishing Accounts for University Professors on Global Scientific Websites (ORCID, Research Gate, Google Scholar)
Venue: arXiv cs.DL 2024
Year: 2024

Perhaps among the most prominent sites on which we always encourage professors to create accounts are (ORCID), (Reserach Gate), and (Google Scholar), and how to publish and promote their research through social media or through educational platforms, conferences, and scientific workshops Then we try to explain, in the course of the research, in a smooth manner, the ways to activate accounts on these platforms, supported by pictures and a comprehensive step-by-step explanation, as a gesture to encourage the spread of the culture of electronic publishing in light of the escalation of the digital and computing revolution and the desire to catch up with its accelerating pace
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis
Venue: arXiv cs.CL 2024
Year: 2024

The widespread availability of code-mixed data can provide valuable insights into low-resource languages like Bengali, which have limited datasets. Sentiment analysis has been a fundamental text classification task across several languages for code-mixed data. However, there has yet to be a large-scale and diverse sentiment analysis dataset on code-mixed Bengali. We address this limitation by introducing BnSentMix, a sentiment analysis dataset on code-mixed Bengali consisting of 20,000 samples with 4 sentiment labels from Facebook, YouTube, and e-commerce sites. We ensure diversity in data sources to replicate realistic code-mixed scenarios. Additionally, we propose 14 baseline methods including novel transformer encoders further pre-trained on code-mixed Bengali-English, achieving an overall accuracy of 69.8% and an F1 score of 69.1% on sentiment classification tasks. Detailed analyses reveal variations in performance across different sentiment labels and text types, highlighting areas for future improvement.
See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses
Venue: arXiv cs.CL 2024
Year: 2024

The impressive performance of Large Language Models (LLMs) has consistently surpassed numerous human-designed benchmarks, presenting new challenges in assessing the shortcomings of LLMs. Designing tasks and finding LLMs' limitations are becoming increasingly important. In this paper, we investigate the question of whether an LLM can discover its own limitations from the errors it makes. To this end, we propose a Self-Challenge evaluation framework with human-in-the-loop. Starting from seed instances that GPT-4 fails to answer, we prompt GPT-4 to summarize error patterns that can be used to generate new instances and incorporate human feedback on them to refine these patterns for generating more challenging data, iteratively. We end up with 8 diverse patterns, such as text manipulation and questions with assumptions. We then build a benchmark, SC-G4, consisting of 1,835 instances generated by GPT-4 using these patterns, with human-annotated gold responses. The SC-G4 serves as a challenging benchmark that allows for a detailed assessment of LLMs' abilities. Our results show that only 44.96\% of instances in SC-G4 can be answered correctly by GPT-4. Interestingly, our pilot study indicates that these error patterns also challenge other LLMs, such as Claude-3 and Llama-3, and cannot be fully resolved through fine-tuning. Our work takes the first step to demonstrate that LLMs can autonomously identify their inherent flaws and provide insights for future dynamic and automatic evaluation.
Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior
Venue: arXiv cs.CV 2024
Year: 2024

The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale ($10^4$-$10^5$ m) satellite images to sub-microscale ($10^0$-$10^1$ m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.
On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning
Venue: arXiv cs.AI 2024
Year: 2024

Conflict-Based Search (CBS) algorithm for the multi-agent pathfinding (MAPF) problem is that it is incomplete for problems which have no solution; if no mitigating procedure is run in parallel, CBS will run forever when given an unsolvable problem instance. In this work, we introduce Temporally-Relative Duplicate Pruning (TRDP), a technique for duplicate detection and removal in both classic and continuous-time MAPF domains. TRDP is a simple procedure which closes the long-standing theoretic loophole of incompleteness for CBS by detecting and avoiding the expansion of duplicate states. TRDP is shown both theoretically and empirically to ensure termination without a significant impact on runtime in the majority of problem instances. In certain cases, TRDP is shown to increase performance significantly
A Primer on Generative AI for Telecom: From Theory to Practice
Venue: arXiv cs.NI 2024
Year: 2024

The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively. We highlight the importance of retrieval augmented generation (RAG) in connecting LLMs to telecom domain specific data sources to enhance the accuracy of the LLMs' responses. We present a real-world use case on RAG-based chatbot that can answer open radio access network (O-RAN) specific questions. The demonstration of the chatbot to the O-RAN Alliance has triggered immense interest in the industry. We have made the O-RAN RAG chatbot publicly accessible on GitHub.
A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges
Venue: arXiv cs.CR 2024
Year: 2024

Mobile applications (apps) have become an essential part of everyday life, offering convenient access to services such as banking, healthcare, and shopping. With these apps handling sensitive personal and financial data, ensuring their security is paramount. While previous research has explored mobile app developer practices, there is limited knowledge about the common practices and challenges that developers face in securing their apps. Our study addresses this need through a global survey of 137 experienced mobile app developers, providing a developer-centric view of mobile app security. Our findings show that developers place high importance on security, frequently implementing features such as authentication and secure storage. They face challenges with managing vulnerabilities, permissions, and privacy concerns, and often rely on resources like Stack Overflow for help. Many developers find that existing learning materials do not adequately prepare them to build secure apps and provide recommendations, such as following best practices and integrating security at the beginning of the development process. We envision our findings leading to improved security practices, better-designed tools and resources, and more effective training programs.